The five critical use cases in the video indicate that AI is transforming retail. This analysis delves into these use cases, revealing how AI’s transformative effects have impacted the industry. The retail scenario is evolving at a rapid rate, and AI has been the key contributor to improved efficiency levels as well as improving customer satisfaction. These applications demonstrate the possibilities of AI transforming retail for customer experience and operations.
Cashier-less Checkout
One of the innovative uses of AI in retail is adopting cashier-less checkout systems. Companies such as Caper AI and Amazon Go are the pioneers of this technology, where customers can enter a store, select their goods from shelves, and leave without standing in lines or having contact with a cashier. Computer vision, IoT sensors, and AI algorithms allow these systems to monitor products’ movements to automate billing (Foodverge, 2022). It has twofold effects – customers save time, and retailers reduce labor expenses. This use case has the potential to radically influence the retail sector by changing the classic checkout experience.
Inventory Management
AI offers solutions for improving inventory management, which is one of the crucial problems among retailers. According to (Moore et al., 2022), The use of computer vision allows retailers to monitor stock levels transparently, locate areas lacking products, and automate replenishment operations. Walmart collaborated with Boston Dynamics to predict products near exhaustion using machine learning, demonstrating the AI potential in inventory control (Foodverge, 2022). This use case is set to streamline supply chain performance, reduce stockouts, and improve overall inventory management for retail businesses.
Detecting Suspicious Activity
AI’s contribution to improving security procedures can be seen in its ability to identify suspicious activities, such as shoplifting and burglary, using sophisticated video analysis techniques. Firms such as VAC in Tokyo have created software that uses activity recognition and pose estimation for detecting suspicious behavior in shops. A 77% reduction in shoplifting losses signifies the impact AI could have on loss prevention (Foodverge, 2022). This will likely become an increasingly important use case as retailers emphasize ensuring security and find new ways to reduce the amount lost through theft.
Customer Experience
Personalizing and understanding the in-store experience is a top priority for retailers; AI plays an integral role in making this happen. Through analysis of customer behavior, dwell time,e and preference, studies using AI can give insights to retailers. Using self-ordering kiosks with facial recognition Cali, Burger shows how AI can make interactions between customers personalized (Foodverge, 2022). According to Oosthuizen et al. (2020), this use case will undoubtedly impact the retail sector by developing customer loyalty, enhancing satisfaction levels, and leading to higher sales if it promotes a tailored experience for them.
Optimizing Store Layout
Image recognition and object detection powered by AI enables the optimization of store layouts and product placements, redefining what it means to shop. Retailers use these technologies to place products strategically and improve the chances of successful sales. Rapid recognition of what shelves are empty ensures timely restocking, a lingering problem in retail (Foodverge, 2022). According to Oosthuizen et al. (2020), It is a promising application that has great potential to revolutionize the overall shopping experience and help retailers make optimum use of their physical spaces.
Self/Peer Assessment and Personal Reflection
Scoring System: 1 (Poor) – 5 (Excellent)
| Team Names | Ideas & Suggestions | Leadership & Task Organization | Information Collection & Collation | Preparing & Delivering Written Reports | Total |
| You | 4 | 5 | 4 | 5 | 18 |
| Teammate 1 | 3 | 4 | 4 | 4 | 15 |
| Teammate 2 | 4 | 3 | 5 | 4 | 16 |
| Teammate 3 | 3 | 4 | 3 | 3 | 13 |
Personal Reflection on Team Assignment
Ideas and Suggestions
There were occasions when I could have encouraged even more creative thought. When thinking about this, I understand the importance of being able to cross creative bounds for a complete solution consistently.
Leadership and Task Organization
Regarding leadership and task organization, our team demonstrated a solid foundation. Coming to a leadership position, I took an integral role in promoting transparent communication and connecting tasks with each member’s strengths. The structured approach allowed for a smooth workflow as we effectively met the deadlines and overcame obstacles. Overall, though, I acknowledge the chance to practice refining my delegation skills so that tasks are distributed fairly among members of any team, and everyone can operate at total capacity. Realizing this has also reinforced my dedication to unceasing improvement and enhancing collaborative relationships for subsequent projects.
Information Collection and Collation
Each participant assumed the responsibility for collecting proper data and verifying its correctness. We used a collaborative platform to share information, creating an open and constructive process. However, in retrospect,t we had opportunities to improve the way of gathering information. This means devising a more efficient system of cross-referring the different sources for data, thereby ensuring that our results are even more precise and logically sound.
Preparing and Delivering Written Reports
A noteworthy accomplishment was that our team did well in preparing and delivering written reports. We developed a solid and productive case study with great care. Clear communication channels made it easy to consolidate individual contributions. The favorable response to our reports emphasized that we all strived for the best job possible. As I look forward, I intend to maintain this level and explore methods of improving the visual impact as well as general interest in our reports. In this forward-looking approach, we aim to keep improving the effectiveness and impact of our team’s communication throughout future projects.
Overall, this team assignment was a beneficial learning process that pointed out our strengths and weaknesses for future development. With a growth mindset, I am eager to incorporate these lessons into future collaborations so as to create an environment of ongoing improvement and shared success.
References
Foodverge (2022) AI In Retail 2023 | Future Of Shopping. Available at: https://www.youtube.com/watch?app=desktop&v=IHBXzwiWvao&embeds_referring_euri=http%3A%2F%2Fforestadaptation2008.net%2F&feature=emb_imp_woyt.
Moore, S., Bulmer, S. and Elms, J. (2022). ‘The social significance of AI in retail on customer experience and shopping practices’. Journal of Retailing and Consumer Services 64(1), p. 102755. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0969698921003210.
Oosthuizen, K., Botha, E., Robertson, J. and Montecchi, M. (2020) ‘Artificial intelligence in retail: The AI-enabled value chain’. Australasian Marketing Journal (AMJ) 29(3). doi: https://doi.org/10.1016/j.ausmj.2020.07.007.
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